arc added

README.md

@@ -2,6 +2,14 @@
 This implements training of deep JSCC models for wireless image transmission as described in the paper [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://ieeexplore.ieee.org/abstract/document/8723589) by Pytorch. And there has been a [Tensorflow and keras implementations ](https://github.com/irdanish11/DJSCC-for-Wireless-Image-Transmission).
 
 This is my first time to use PyTorch and git to reproduce a paper, so there may be some mistakes. If you find any, please let me know. Thanks!
+## Architecture
+
+![architecture](./demo/arc.png)
+
+## Demo
+
+
+
 
 ## Installation
 conda or other virtual environment is recommended.
@@ -19,13 +27,17 @@ cd ./Deep-JSCC-PyTorch
 ```
 
 ```
-python train.py --lr 10e-4 --epochs 100 --batch_size 32 --channel 'AWGN' --saved ./saved --snr_list [1,4,7,13,19] --ratio_list [1/6,1/12] --dataset imagenet
+python train.py --lr 10e-4 --epochs 100 --batch_size 32 --channel 'AWGN' --saved ./saved --snr_list 1 4 7 13 19 --ratio_list 1/6 1/12 --dataset imagenet
 ```
 or
 ```
-python train.py --lr 10e-3 --epochs 100 --batch_size 64 --channel 'AWGN' --saved ./saved --snr_list [1,4,7,13,19] --ratio_list [1/6,1/12] --dataset cifar10
+python train.py --lr 10e-3 --epochs 100 --batch_size 512 --channel 'AWGN' --saved ./saved --dataset cifar10 --num_workers 4 --parallel True
 ```
 ### Evaluation
+Run(example presented in paper)
+```
+python eval.py --channel 'AWGN' --saved ./saved/${mode_path} --snr 20 --ratio_list 1/3 --test_img ./test_image ./demo/kodim08.png
+```
 
 
 ## Citation